Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer-aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co-occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.

A novel classification scheme to decline the mortality rate among women due to breast tumor.

Accurate tumor segmentation is an essential and crucial step for computer-aided brain tumor diagnosis and surgical planning. Subjective segmentations are widely adopted in clinical diagnosis and treating, but they are neither accurate nor reliable. An automatical and objective system for brain tumor segmentation is strongly expected. But they are still facing some challenges such as lower segmentation accuracy, demanding a priori knowledge or requiring the human intervention. In this paper, a novel and new coarse-to-fine method is proposed to segment the brain tumor. This hierarchical framework consists of preprocessing, deep learning network based classification and post-processing. The preprocessing is used to extract image patches for each MR image and obtains the gray level sequences of image patches as the input of the deep learning network. The deep learning network based classification is implemented by a stacked auto-encoder network to extract the high level abstract feature from the input, and utilizes the extracted feature to classify image patches. After mapping the classification result to a binary image, the post-processing is implemented by a morphological filter to get the final segmentation result. In order to evaluate the proposed method, the experiment was applied to segment the brain tumor for the real patient dataset. The final performance shows that the proposed brain tumor segmentation method is more accurate and efficient.

A deep learning-based segmentation method for brain tumor in MR images

In order to begin the initial check on breast cancer, radiologist can use Computer Aided Diagnosis (CAD) as another option to detect breast cancer. During breast cancer check, human error is often to affecting the result. Several research before have proved that CAD is able to detect breast cancer spot more accurate. The purpose of this research is to find reliable method to classify breast cancer abnormalities. Mammography Image Analysis Society (MIAS) database is used as the sample data to the proposed system in this research. Mammograms are divided into three categorize which are normal, benign and malignant according to MIAS database. Features included in this experiment are extracted by using gray level co-occurrence matrices (GLCM) at 0o, 45o, 90o and 135o with a block size of 128x128. In classification process, this research attempt to compare k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifier in order to achieve the better accuracy. The result shows that SVM outperforms KNN in breast cancer abnormalities classification with 93.88% accuracy.

Comparison Classifier: Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) In Digital Mammogram Images

A new approach for the detection of mammary calcifications by using the white Top-Hat transform and thresholding of Otsu

Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images.

Mammography images are employed in diagnosing breast cancers, since they are most effective, low cost and one of the highly sensitive techniques such that they can detect even small lesions. The proposed work increases the accuracy of classification and reduces the percentage of false positives. The images from the data set are initially preprocessed and contrast enhanced which makes the image most effective for further analysis. Then Region Of Interest (ROI) is determined from morphological top hat filtered image by means of thresholding segmentation. Various features like first order textural features, Gray Level Co-occurrence Matrix (GLCM) features, Discrete Wavelet Transform (DWT) features, run length features and higher order gradient features are derived for the particular ROI. Support Vector Machine (SVM) classifier is trained with the above mentioned features using MATLAB bioinformatics tool box. Thus the classified results are obtained for the query image based on the trained SVM structure. The mammography data set has been taken from the Mammographic Image Analysis Society (MIAS) in which there are 322 images available along with ground tooth information.

Automatic detection of tumor subtype in mammograms based On GLCM and DWT features using SVM

The term brilliant accuracy agribusiness alludes to advances like the Internet of Things, remote sensors, and artificial reasoning on the ranch. A definitive objective of this paper is to expand the quality and amount of the harvests while advancing the human work utilized, use of water system, and diminishing the water system times in light of climate forecast. This proposed framework utilizes the three kinds of sensors like Moisture sensor is used to detect the dirt dampness, the Humidity sensor is utilized to return how much water is present in the encompassing air, Temperature sensors are utilized to give the temperature of the dirt. Each of the three qualities is passed to the remote sensor hub and moved to the information lumberjacks. At long last, the information lumberjack will send the message like beginning the water system to a programmed water lock framework. When all water measures arrive at the water system limit, the programmed water lock framework will be shut. By using the underground, remote sensors, we can track the conditions of various agricultural applications such as soil properties, seed varieties and monitor the environmental situations. Every gadget contains significant gear like sensors, memory, a processor, and a power source.

Smart Precision Irrigation Techniques Using Wireless Underground Sensors in Wireless Sensors

Optimising hyper-parameters in Random Forest is a time-consuming undertaking for several academics as well as professionals. To acquire greater performance hyper-parameters, specialists should explicitly customize a series of hyper-parameter settings. The best outcomes from this manual setting are then modelled and implemented in a random forest algorithm. Several datasets, on the other side, need various prototypes or hyper-parameter combinations, which may be time-consuming. To solve this, we offered various machine learning models and classifiers for correctly optimising hyper-parameters. Both genetic algorithm-based random forest and randomised CV random forest were assessed on performance measures such as sensitivity, accuracy, specificity, and F1-score. Finally, when compared to randomised CV random forest, our suggested model genetic algorithm-based random forest delivers more incredible accuracy.

IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification

Crop yield production value update has a beneficial practical impact on directing agricultural production and informing farmers of changes in crop market prices. The main objective of the suggested method is to put the crop selection technique into practise so that it may be used to address a variety of issues facing farmers and the agricultural industry. As a result, the yield rate of crop production is maximised, which benefits our Indian economy. land conditions of several kinds. So, using a ranking system, the quality of the crops are determined. This procedure also alerts farmers to the rate of crops of low and high quality. Due to the use of multiple classifiers, using an ensemble of classifiers paves the way for better prediction decisions. The decision-making process for selecting the output of the classifiers also incorporates a rating system. The price of a crop that will produce more is predicted using this method.

Identifying a Range of Important Issues to Improve Crop Production

The hardware floating point arithmetic units are needed for the scientific operations and high-speed data signal processing. This paper proposes very high speed floating point arithmetic unit for all complex data with the size of 64 bits and due to pipelining concept, the performance in the speed is improved for all the arithmetic and logical operations The reason of considering floating point numbers is good in resolution, higher dynamic range, no scaling needed and accuracy which is more attracted for complicated problems compared to fixed point though it has higher speed and lower cost characteristics. Single and double precision IEEE 754 standard are used to perform the required calculations and implemented using Verilog HDL and each module functionality is tested using test vectors. This method of approach for computing complex numbers with double precision floating point unit. The results on this proposed system are obtained in the parameters like delay, area and memory and proves of high efficiency and reduces critical path delay. This floating-point complex algorithm is simulated using Modelsim 6.4c, Xilinx tool is used to synthesize and executed in FPGA Spartan 3 XC3S 200 TQ-144.

S. Thaiyalnayaki

Papers

Automatic detection of tumor subtype in mammograms based On GLCM and DWT features using SVM

Smart Precision Irrigation Techniques Using Wireless Underground Sensors in Wireless Sensors

IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification

Identifying a Range of Important Issues to Improve Crop Production

Design of 64-bit Floating-Point Arithmetic and Logical Complex Operation for High-Speed Processing